Database Tuning: 7 Performance Tips for PostgreSQL & MySQL

Introduction

Whether you’re managing a bustling e-commerce platform or a real-time analytics dashboard, database performance is a make-or-break factor for your applications. If your PostgreSQL or MySQL instance starts lagging under load, end-user experience can take a serious hit - and so can your credibility with stakeholders.

After more than two decades of hands-on experience tuning databases for everything from lean startups to enterprise-scale systems, I can tell you this: performance doesn’t just happen - it’s engineered. PostgreSQL and MySQL each bring unique tools to the table, but both benefit from structured tuning techniques that are grounded in real-world scenarios.

In this practical guide, I’ll walk you through seven essential strategies that have repeatedly delivered results in production environments. We’ll cover advanced query optimization, smart indexing decisions, connection pooling tactics, the role of caching, and using monitoring tools like pg_stat_statements and MySQL’s slow query log to pinpoint exactly what’s slowing things down. Whether you’re troubleshooting a slow query or building for scale from day one, consider this your cheat sheet.

1. Optimize Queries Like a Pro

You can throw all the RAM and SSDs you want at a slow query, but if your SQL isn’t tuned, you’re swimming upstream. Trust me - I’ve seen beautifully specced instances brought to their knees by one rogue query.

What to do:

• Always check the query plan first. For PostgreSQL, use:

EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM orders WHERE customer_id = 1024;

In MySQL:

EXPLAIN FORMAT=JSON SELECT * FROM orders WHERE customer_id = 1024;

Look out for full table scans where indexes should be used.

• Replace correlated subqueries when possible with joins. Correlated subqueries can be murder on performance in large datasets.

• Use bind variables and prepared statements, especially for high-throughput transaction workloads. Not only does this reduce parsing overhead, it improves cache utilization.

• Be careful with SELECT *. Pulling in all columns - even ones you don’t use - burns I/O and memory.

• Materialize expensive joins or calculations you can offload. PostgreSQL’s materialized views or a caching layer can simplify recurring heavy queries.

• Track the worst offenders. PostgreSQL’s pg_stat_statements and MySQL’s Performance Schema give you pricey query patterns on a platter.

2. Build Indexes with Intent

A good index can take a query from minutes to milliseconds - but indexing without a plan can kill write performance and bloat storage.

Key tactics:

• Understand your query access patterns before adding indexes. Don’t just index everything - index what matters.

• Composite indexes can be lifesavers. If your query filters on both last_name and first_name, a composite index on (last_name, first_name) will outperform individual ones.

• Consider BRIN or GIN indexes in PostgreSQL for specific use cases like time-series (BRIN) or JSONB fields (GIN).

• Use partial indexes when it makes sense. For example, if 90% of your queries hit active users, a partial index on WHERE status = 'active' keeps it lean and fast.

• Avoid duplicated and unused indexes. These are silent killers. Use pg_stat_user_indexes (PostgreSQL) and information_schema.STATISTICS (MySQL) to find and drop excess.

• Covering indexes (indexes that include all selected columns) are great for SELECT-only queries. MySQL’s INCLUDE clause and PostgreSQL’s INCLUDE (since v11) allow building these optimally.

3. Don’t Burn Connections - Pool Them

I’ve lost count of the number of production outages I’ve seen caused by too many open connections. Applications often mismanage this aspect, and it quietly eats away at performance until connections stop being accepted.

Here’s the plan:

• Use connection pooling. For PostgreSQL, PgBouncer can be a game-changer. For MySQL, application-level pooling with tools like HikariCP (Java), mysql2 (Node.js), or middleware like ProxySQL does the trick.

• Keep pool sizes reasonable. Bigger isn’t better here. Oversized pools overload the DB with idle connections. Size based on expected concurrency and memory budget.

• Use transaction pooling in PgBouncer, especially if you handle short transactions. It can drastically reduce the total count of backend connections.

• Monitor pool stats. Queue lengths, connection reuse, and wait times can tell you if you’re maxing out too often.

4. Cache Strategically, Not Reactively

A lot of folks tack caching on after they’ve already run into performance problems. Done right, caching should be part of performance architecture from the jump.

Best practices:

• Cache at the right level. Redis or Memcached are great for application-level results, while NGINX/Varnish work well for HTTP responses.

• In-database caching still matters. In PostgreSQL, things like shared_buffers determine how much fall-through goes to disk. Make sure it’s tuned (more on that soon).

• Use materialized views when query content does not need to be real-time. For example, pre-aggregated sales data can update once an hour.

• Implement solid invalidation logic. Outdated cache is often worse than no cache at all.

• Use prepared statement caching at the driver level. For example, JDBC and Node.js drivers often offer this and can save milliseconds per request.

5. Keep a Watchful Eye with Monitoring Tools

“You can’t fix what you can’t see.” That holds especially true for database workloads.

Get granular:

• PostgreSQL: Turn on pg_stat_statements and set log_min_duration_statement to surface slow queries. Combine with Prometheus exporters to feed metrics to Grafana.

• MySQL: Enable slow logs and the Performance Schema. Monitor key metrics like Threads_running, InnoDB buffer usage, and index hit ratios using Percona Monitoring and Management (PMM).

• Dashboard everything. Correlate query latency, cache hit rate, active connections, and CPU utilization. That’s how you catch spikes before PagerDuty lights up.

• Explain your way out of trouble. When in doubt, run EXPLAIN - even better, keep a history so you can detect regressions after schema or data changes.

6. Tune Configs for the Hardware You Actually Have

Out-of-the-box configs are conservative - and nowhere near optimal for most serious workloads.

What to tweak:

• PostgreSQL:

• shared_buffers (~25 - 40% of total RAM)

• work_mem (~2 - 4MB per sort operation, scale as needed)

• effective_cache_size (usually 70% of RAM)

• MySQL:

• innodb_buffer_pool_size (typically 70-80% RAM on dedicated instances)

• innodb_log_file_size (consider increasing for write-heavy workloads)

• innodb_flush_log_at_trx_commit = 2 (safe and faster than 1 for many apps)

• Parallelism: PostgreSQL’s max_parallel_workers_per_gather lets you use multiple CPU cores for large queries (great since v13+).

• Track write amplification: In PostgreSQL, poor checkpoint tuning (checkpoint_timeout, max_wal_size) can cause sudden I/O bursts.

• Watch thread contention on high-concurrency MySQL workloads. Tweak innodb_thread_concurrency and thread_cache_size.

7. Backups That Don’t Burn Performance

Your backup strategy shouldn’t bring your production DB to its knees. And yet, I’ve watched large snapshot-based backups disrupt user-facing APIs mid-day just because no one scheduled things properly.

Smarter approaches:

• Use streaming backups like pg_basebackup or wal-g in PostgreSQL for minimal disruption.

• MySQL users: lean on Percona XtraBackup for non-blocking physical backups.

• Schedule backups during off-peak hours - and adjust based on region if you’re globally distributed.

• Keep an eye on replication lag, especially during backup windows. Disk I/O contention often delays syncing, which can cause stale reads.

• Backups ≠ high I/O penalty if done right. Separate backup I/O from production disks where possible.

Common Pitfalls & Proven Fixes

Performance Tuning Checklist

• Are your most frequent queries optimized and indexed?
• Do you log and monitor slow queries?
• Is connection pooling in place and properly tuned?
• Are caches used where possible, and invalidated correctly?
• Is your configuration aligned with available hardware?
• Are you doing backups without killing cluster I/O?
• Is monitoring in place to catch regressions early?

Further Reading & Tools

• PostgreSQL Docs
• MySQL Performance Schema
• PgBouncer
• ProxySQL
• Explain.depesz.com
• Percona Monitoring and Management

Need to dig deeper into infrastructure? Check out:

• INFOiYo: Linux systemd service management
• INFOiYo: Rootless Containers Guide

Final Thoughts

Database tuning is both art and science. No single checklist will future-proof your environment, but applying these principles consistently will build a resilient, high-performing system backed by data and experience rather than guesswork.

Whether you’re untangling a misbehaving query or planning for scale, remember: fast databases aren’t born - they’re built.

Keep your logs clean, your pools slim, your caches relevant, and your queries sharp.

Happy tuning!